BANISHING THE THEORY-APPLICATIONS DICHOTOMY FROM STATISTICS EDUCATION

Larry WeldonDepartment of Statistics and Actuarial Science

Simon Fraser University, Burnaby, CANADA

“Issue” Questions

• Is Mathematical Statistics = Theory of Statistics?• Expert vs Practitioner vs Generalist

different stats education?• Motivation for practitioner grps?• What undergrad course sequences?– for practitioners– for experts

• Motivation for Stats Instructors?

??

Implications for Stats Course Taxonomy

Some Questions

• Is Mathematical Statistics = Theory of Statistics?• Expert vs Practitioner vs Generalist

different stats education?• Motivation for practitioner grps?• What undergrad course sequences?– for practitioners– for experts

• Motivation for Stats Instructors?

??

Basic Theory: More than Math?

• Obs Study vs Experiment• Distributions: Averages and Variability• Random Sampling, Estimation• Independence (and dependence)• Time Series• Statistical Significance

Example: Dependence

When does a portfolio of stocks have enough independence to provide stability of return?

One needs to understand the dependence-independence concept

A & B independent -> P(A&B)=P(A)*P(B) is not enough

Basic Theory: More than Math?

• Obs Study vs Experiment• Distributions: Averages and Variability• Random Sampling, Estimation• Independence (and dependence)• Time Series• Statistical Significance

Theory = Generally Applicable Concepts(Much more than Mathematics)

Question Answered?

• Is Mathematical Statistics = Theory of Statistics?

• No! Theory is Generally Applicable Concepts.

??

More Questions ->

Some Questions

• Is Mathematical Statistics = Theory of Statistics?• Expert vs Practitioner vs Generalist

different stats education?• Motivation for practitioner grps?• What undergrad course sequences?– for practitioners– for experts

• Motivation for Stats Instructors?

??

Levels of Expertise• Generalist– requires stats appreciation

• Practitioner– requires stats appreciation– requires stats methods & hazards– requires exposure to expert capability

• Expert– all the above and much more

Cumulation Model of Statistics Education

Do Practitioners need “Appreciation” Course?

• Overview for when-to-consult• Motivation to integrate with applied focus• Awareness of naïve user (hazards)

Experts need “stats appreciation”?

• Yes, because they need informed choice of career

• Real expert statisticians are generalists as well as specialists, so they can absorb context

• Need to explain to naïve user

Experts need “Practitioner” training?

• of course!• early exposure helps education• no need to learn everything the hard way

Proposed Course Sequence:

Appreciation -> Practitioner -> Expert

Questions ->

Some Questions

• Is Mathematical Statistics = Theory of Statistics?• Expert vs Practitioner vs Generalist

different stats education?• Motivation for practitioner grps?• What undergrad course sequences?– for practitioners– for experts

• Motivation for Stats Instructors?

??

Motivation Clusters?

• Does “auto engine size” or “golf participation” interest biologists?

• Does “potato pest resistance” or “threatened species of birds” interest social scientists?

Contextual Interest is Important for Seeking Data-Based Information

Stats Streams for Major Groups?

• General (Wide Focus)• Life Science• Social Science

Important for early courses, perhaps not feasible for higher level ones.

Context Material Matters! Because Context-Major Students chose context!

Minimal Context Segregation for Courses …

(segregation by context …not by methods introduced)

Questions ->

Some Questions

• Is Mathematical Statistics = Theory of Statistics?• Expert vs Practitioner vs Generalist

different stats education?• Motivation for practitioner grps?• What undergrad course sequences?– for practitioners– for experts

• Motivation for Stats Instructors?

??

Undergrad Course Structure?

• Statistics 1 (life) Statistics 1 (social) Statistics 1 (general)(Appreciation courses)

• Statistics 2 (life) Statistics 2 (social) Statistics 2 (general)• Statistics 3 (life) Statistics 3 (social) Statistics 3 (general)

(Practitioner Courses)• Statistics 4 (general)• Statistics 5 (general)• Statistics 6 (general)

(Expert courses)

More courses where numbers permit.

Note: 1. No specialized technique courses like Nonparametrics, Time Series, Experimental Design, Quality Control, Bayesian Analysis2. No “service” stream 3. No “baby” stat courses

Experts need “MORE” not “DIFFERENT”

Experiential Learning&Teaching

• Sequence of Projects– data collection– data analysis– data summary

• Techniques as Required• Concepts as they Arise

Example ->

Experiential Learning Examples• Sports Leagues– probability– measures of variability– simulation

• Daily Delivery Schedules– censored data (demand exceeds sales)– parametric variability, prediction– optimization

Many concepts and techniques will be introduced

Questions ->

Some Questions

• Is Mathematical Statistics = Theory of Statistics?• Expert vs Practitioner vs Generalist

different stats education?• Motivation for practitioner grps?• What undergrad course sequences?– for practitioners– for experts

• Motivation for Stats Instructors?

Motivation for Stats Instructors?

• Case Studies/Projects – experiential learning• Discussion & Presentations• Novelty and Creativity encouraged• Active engagement of students and instructors• Better Use of Instructor Expertise & Experience

Motivation for Stats Instructors?

• Case Studies/Projects – experiential learning• Discussion & Presentations• Novelty and Creativity encouraged• Active engagement of students and instructors• Better Use of Instructor Expertise & Experience

Motivation for Stats Instructors?

• Case Studies/Projects – experiential learning• Discussion & Presentations• Novelty and Creativity encouraged• Active engagement of students and instructors• Better Use of Instructor Expertise & Experience

Motivation for Stats Instructors?

• Case Studies/Projects – experiential learning• Discussion & Presentations• Novelty and Creativity encouraged• Active engagement of students and instructors• Better Use of Instructor Expertise & Experience

Motivation for Stats Instructors?

• Case Studies/Projects – experiential learning• Discussion & Presentations• Novelty and Creativity encouraged• Active engagement of students and instructors• Better Use of Instructor Expertise & Experience

Summary• Experiential Learning is Authentic Learning• It can be motivating for most students and

instructors• It can be efficient in reducing the number of

courses offered• Levels of expertise correspond to number of

courses completed (not math level)• Downside? Requires instructors with an interest

in, and experience with, using statistical theory.

Thanks for attending this session. Comments? weldon@sfu.ca